Introducing Vectry: causal data infrastructure
Why we built a data layer where every event is attributable, every outcome reconstructible, and every anomaly explainable.
Blog
Notes from the team building Vectry.
Causal observability, engineering notes and product updates from the team building Vectry Analytics.
The Vectry data model: five primitives, one causal graph
A walk through Event, Trace, CausalThread, EventExplanation and Anomaly — the five primitives everything in Vectry reduces to.
Why causal observability: logs tell you what, not why
Observability solved detection. Explanation is still archaeology. The case for capturing causality at write time.
Instrumenting a Node.js service with @vectry/node
A practical guide to event design: namespaces, the operation grammar, change diffs, and capture patterns that never block your request path.
One thread across six services
How CausalThreads reconstruct a cross-system storyline — from the first event to the final outcome — without asking your services to know about each other.
Anomalies that explain themselves
Deviation scores are easy. Explained deviations are the product: expected pattern, actual pattern, and the causal chain behind every break.
The causal backbone of StoneOS
How Vectry Analytics, Veripass and Sommatic compose: identity grounds the events, workflows emit them, and the causal graph explains the whole operation.
Stop guessing. Start explaining.
Bring causal infrastructure to your operation — or start instrumenting with the open-source SDKs today.